Research On MIMO Detection

Multiple-Input Multiple-Output (MIMO) technique is an effective way to achieve high capacity and (or) to improve the reliability. The design of MIMO detector can greatly affect the data rate, bit error ratio and the computational complexity. Based on the tradeoff between the performance and the complexity, this dissertation is intended to design effective and efficient MIMO detectors that are applicable for different channel conditions (full-rank or rank-deficient channel; correlated or uncorrelated channel) with any antenna configurations. The main results are summarized as followed.1. Investigate the efficient implementation of the soft-input soft-output MMSE detector for Turbo-MIMO systems and a simplified MMSE detector is proposed. By employing the matrix inversion lemma and the singular value decomposition of the channel matrix, the proposed algorithm decreases complexity greatly and can be applicable for any constellation.2. Based on the successive interference cancellation algorithm using QR decomposition, the MMSE preprocessing is introduced to form an equivalent upper triangular channel. By employing the MMSE preprocessing and exposing the underlying bit mapping structure of QAM signals, a bit-level tree structure can be obtained. When tree search algorithm is applied to this bit-level tree structure, a bit-level tree search algorithm is obtained. The proposed bit-level tree search algorithm can be employed in the rank-deficient MIMO systems. Compared with the original tree-search algorithm, the proposed algorithm can greatly reduce the computational complexity when high order constellation is employed. In addition, the proposed algorithm can utilize the a priori information from the decoder to further reduce the computational complexity.3. The detection ordering algorithm in the tree search detection is investigated and the ordering algorithm employing log-likelihood ratio information in the tree search detection is proposed. The novel algorithm can fully utilize the received signal, channel state information and a prior information to determine the detection order. The performance of the tree search detection can be improved by the ordering algorithm. 4. The group detection strategy operates by dividing the set of transmitted symbols into small groups. In the proposed iterative group MAP detection algorithm, information obtained by the detected groups is shared by other groups. After all the groups are detected, the information about all the transmitted signals can be feed back to the first detected group to improve the performance; all the other groups are detected again. The algorithm works in an iterative way. The iterative group MAP detection can fully utilize the information obtained by the detectors and it achieves a better performance compared with the group MAP detection. Its complexity can be controlled by the group number and iteration number.5. A soft-output detector with lattice-reduction (LR) is proposed. LR can transform the system model into an equivalent one with better condition. LR algorithm provides a good initial estimation of the transmitted signals. The boundary of signals in the reduced lattice for each layer can be obtained. After the boundary and the initial estimation are obtained, the stack algorithm can be employed to search the candidate list. When the list is generated, the soft bit of the transmitted signals can be calculated. Simulation results show that compared with other detectors, the proposed algorithm can provide the same performance with significantly reduced computational complexity.